Basic features of the system

Search features

OMEGA-PSIR is equipped with very powerful seach capabilities, based on the SOLR technology. For each object type there is a screen with simple search (google-like), and advanced search. The index is built from metadata records and, whenever applicable, from the attached full text documents.

Objects in the Knowledge Base are connected to each other using various relationships, so that they create a semantic network, which can be see as a graph structure. Searching in the system can be performed in two ways (see https://omega-psir.atlassian.net/wiki/spaces/OMEN/pages/5577802050 ):

• by taking into account the graph structure and indicating in the query how particular types of objects should be combined - this is a graph search

• by ignoring the graph structure and treating individual network elements as related texts, the text of the publication's description is "extended" by the text of the person's description and we perform a search in such extended texts - we then have a text search.

The search results can be adjusted to the user profile, so the answer can be built taking into account the researcher’s “fingerpring” of his/her research. What is very specific for the system, one can use the obtained answer for building a specific report (a simple bibliography listing, an advanced pivot table revealing knowledge hidden in the collected data, such as e.g. distribution of the research teams contribution to a given research subject).

Profiles

As OMEGA-PSIR covers functionality of Research Profiling, one of the main features of the system is automatic building various profiles, such as the profiles of researchers, and units constituting the formal hierarchical structure of the university.

The profiles characterize research achievements of the body, providing lists of publications, patents, promoted theses, activities, prizes and awards, bibliometric measures of the research, cooperation information and statistics. Once an achievement record of a researcher (publication, thesis, patent) is created (and/or deposited), it automatically appears in the profile of the researcher, and in the profile of his /her affiliation profile, as well as in the profiles on all the higher hierarchy levels. If the achievement has coathors, it also goes automatically to the coauthors and their afiliations profiles.

The entered achievements are used to automatically build a cloud of tags at researcher’s profiles and all the units profiles. The cloud of tags is a kind of “fingerprint” characterizing very precisely the research area of the researcher. In addition to the researchers and units profiles, one can define profiles for the main scientific disciplines that are practiced at the university. These profiles may go across the faculties and departments.

Statistics and reports

In OMEGA-PSIR statistics are everywhere. Various statistics are publicly available: some are available when you display a publication record where you can find altmetrics measures, or number of citations by Scopus or WoS; other aggregates can be found on the researchers profiles, or unit profiles, where one can also generate a ready-to-use report. The main idea is that having performed a search you can ”send” the answer to the system as an input and get a report generated as a result. The most powerful tool is a pivot table, it can be used in a flexible way to drill down into data to extract interesting trends; Other available tools, less dynamic, are predefined standard report for a periodical monitoring of the research.

Data management

In OMEGA-PSIR the data management facilities are very powerful. The system provides means for

1. importing data from various sources, both bibliographic and auxiliary ones (which makes the data entry process much cheaper). For example data can be imported from global scientific database, like e.g. SCOPUS, WoS, PubMed), from patents database (EPO), CrossRef, DataCite, ORCID, also from other OMEGA-PSIR based knowledge bases (which makes possible building the federated portals, see Polish Medical Platform);

2. enriching existing metadata with data from external resources (e.g. enriching bibliographic metadata with bibliometric data from Scopus, WoS, or integrating journals with Sherpa-Romeo to get information about openess strategy of the journals),

3. acquiring multimedia data from Youtube

4. importing XML files from web or from a desktop - special tools are available to translate XML to the requested formats.

5. Manual data entry by librarians and trained users

6. self depositing data by researchers

7. One can define workflow for data entry processes

8. Strong duplicate discovery procedures are available

For manual data entry staff strong validation control tools are provided. The validation procedures aredefinable by the system administration.

Roles and priviledges of the users

The system provides definable roles wchich then can be assigned to the system users. The roles specify which functions on particular objects are available. Additionally the system makes possible defining the scope of the access by assiging to the user a hierarchy level within the university structure, so that, for example, a given user is responsible for specific data type(s), but only for a given faculty.

Flexibility of the system

The system flexibility is the result of the applied architecture:

1. One can easily add new object types or change the structure of an existing one;

2. One can define ways of assessing research by means of scripts prepared in drools

3. one can define validation rules, data entry forms, search screens, etc

4. For the existing functions there is a toggle system, making it possible to switch on/off any function for all users, or only at the public level